{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "from sklearn.datasets import fetch_mldata\n",
    "import random\n",
    "import cntk as C\n",
    "import cntk.tests.test_utils\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import argparse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_training_samples = 60000 # Number of training samples\n",
    "batch_size = 64 # Number of mini-batch size\n",
    "num_epochs = 10 # Number of epochs of data for training\n",
    "initial_learning_rate = 0.1 # Initial learning rate\n",
    "train_log_iter = 500 # Number of iteration per training log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Required Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the class for mini-batch reader in random fashion.\n",
    "class Batch_Reader(object):\n",
    "    def __init__(self, data , label):\n",
    "        self.data = data\n",
    "        self.label = label\n",
    "        self.num_sample = data.shape[0]\n",
    "\n",
    "    def next_batch(self, batch_size):\n",
    "        index = random.sample(range(self.num_sample), batch_size)\n",
    "        return self.data[index,:].astype(np.float32),self.label[index,:].astype(np.float32)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data.\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "\n",
    "# Create train & test data.\n",
    "train_data = mnist.data[:num_training_samples,:]\n",
    "train_label = mnist.target[:num_training_samples]\n",
    "test_data = mnist.data[num_training_samples:,:]\n",
    "test_label = mnist.target[num_training_samples:]\n",
    "\n",
    "# Transform train labels to one-hot style.\n",
    "enc = OneHotEncoder()\n",
    "enc.fit(train_label[:,None])\n",
    "onehotlabels_train = enc.transform(train_label[:,None]).toarray()\n",
    "\n",
    "# Call and create the ``train_reader`` object.\n",
    "train_reader = Batch_Reader(train_data, onehotlabels_train)\n",
    "\n",
    "# Transform test labels to one-hot style.\n",
    "enc = OneHotEncoder()\n",
    "enc.fit(test_label[:,None])\n",
    "onehotlabels_test = enc.transform(test_label[:,None]).toarray()\n",
    "\n",
    "# Call and create the ``test_reader`` object.\n",
    "test_reader = Batch_Reader(test_data, onehotlabels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " average      since    average      since      examples\n",
      "    loss       last     metric       last              \n",
      " ------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Architecture parameters\n",
    "feature_dim = 784\n",
    "num_classes = 10\n",
    "num_hidden_layers = 3\n",
    "hidden_layer_neurons = 400\n",
    "\n",
    "# Place holders.\n",
    "input = C.input_variable(feature_dim)\n",
    "target = C.input_variable(feature_dim)\n",
    "\n",
    "# Creating the architecture\n",
    "def create_model(features):\n",
    "    '''\n",
    "    This function creates the architecture model.\n",
    "    :param features: The input features.\n",
    "    :return: The output of the network which its dimentionality is num_classes.\n",
    "    '''\n",
    "    with C.layers.default_options(init = C.layers.glorot_uniform(), activation = C.ops.relu):\n",
    "\n",
    "            # Hidden input dimention\n",
    "            hidden_dim = 64\n",
    "\n",
    "            # Encoder\n",
    "            encoder_out = C.layers.Dense(hidden_dim, activation=C.relu)(features)\n",
    "            encoder_out = C.layers.Dense(int(hidden_dim / 2.0), activation=C.relu)(encoder_out)\n",
    "\n",
    "            # Decoder\n",
    "            decoder_out = C.layers.Dense(int(hidden_dim / 2.0), activation=C.relu)(encoder_out)\n",
    "            decoder_out = C.layers.Dense(hidden_dim, activation=C.relu)(decoder_out)\n",
    "            decoder_out = C.layers.Dense(feature_dim, activation=C.sigmoid)(decoder_out)\n",
    "\n",
    "            return decoder_out\n",
    "\n",
    "# Initializing the model with normalized input.\n",
    "net = create_model(input/255.0)\n",
    "\n",
    "# loss and error calculations.\n",
    "target_normalized = target/255.0\n",
    "loss = -(target_normalized * C.log(net) + (1 - target_normalized) * C.log(1 - net))\n",
    "label_error  = C.classification_error(net, target_normalized)\n",
    "\n",
    "# Instantiate the trainer object to drive the model training\n",
    "lr_per_sample = [0.0001]\n",
    "learning_rate_schedule = C.learning_rate_schedule(lr_per_sample, C.UnitType.sample, epoch_size=int(num_training_samples/2.0))\n",
    "\n",
    "# Momentum\n",
    "momentum_as_time_constant = C.momentum_as_time_constant_schedule(200)\n",
    "\n",
    "# Define the learner\n",
    "learner = C.fsadagrad(net.parameters, lr=learning_rate_schedule, momentum=momentum_as_time_constant)\n",
    "\n",
    "# Instantiate the trainer\n",
    "progress_printer = C.logging.ProgressPrinter(0)\n",
    "train_op = C.Trainer(net, (loss, label_error), learner, progress_printer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate per sample: 0.0001\n",
      "      544        544      0.837      0.837            64\n",
      "Minibatch: 0, Loss: 543.712, Error: 83.75%\n",
      "      543        543      0.862      0.875           192\n",
      "      541        540       0.84      0.823           448\n",
      "      505        474      0.776       0.72           960\n",
      "      377        256      0.652      0.536          1984\n",
      "      295        215      0.552      0.455          4032\n",
      "      246        198      0.454      0.357          8128\n",
      "      209        172      0.335      0.217         16320\n",
      "Minibatch: 500, Loss: 134.738, Error: 10.74%\n",
      "      175        141      0.242       0.15         32704\n",
      "Minibatch: 1000, Loss: 108.200, Error: 3.06%\n",
      "      147        118      0.163     0.0846         65472\n",
      "Minibatch: 1500, Loss: 101.279, Error: 5.69%\n",
      "Minibatch: 2000, Loss: 90.768, Error: 2.53%\n",
      "      125        103       0.11     0.0563        131008\n",
      "Minibatch: 2500, Loss: 95.325, Error: 7.22%\n",
      "Minibatch: 3000, Loss: 87.568, Error: 4.23%\n",
      "Minibatch: 3500, Loss: 89.706, Error: 4.01%\n",
      "Minibatch: 4000, Loss: 87.201, Error: 1.58%\n",
      "      108       90.9     0.0736     0.0374        262080\n",
      "Minibatch: 4500, Loss: 85.550, Error: 4.61%\n",
      "Minibatch: 5000, Loss: 82.218, Error: 1.41%\n",
      "Minibatch: 5500, Loss: 81.526, Error: 2.62%\n",
      "Minibatch: 6000, Loss: 83.215, Error: 3.17%\n",
      "Minibatch: 6500, Loss: 85.671, Error: 2.99%\n",
      "Minibatch: 7000, Loss: 83.309, Error: 2.41%\n",
      "Minibatch: 7500, Loss: 81.284, Error: 1.28%\n",
      "Minibatch: 8000, Loss: 71.777, Error: 1.01%\n",
      "     95.2       82.4     0.0509     0.0282        524224\n",
      "Minibatch: 8500, Loss: 79.828, Error: 1.66%\n",
      "Minibatch: 9000, Loss: 75.310, Error: 1.40%\n"
     ]
    }
   ],
   "source": [
    "# Plot data dictionary.\n",
    "plotdata = {\"iteration\":[], \"loss\":[], \"error\":[]}\n",
    "\n",
    "# Initialize the parameters for the trainer\n",
    "num_iterations = (num_training_samples * num_epochs) / batch_size\n",
    "\n",
    "# Training loop.\n",
    "for iter in range(0, int(num_iterations)):\n",
    "\n",
    "    # Read a mini batch from the training data file\n",
    "    batch_data, batch_label = train_reader.next_batch(batch_size=batch_size)\n",
    "\n",
    "    arguments = {input: batch_data, target: batch_data}\n",
    "    train_op.train_minibatch(arguments=arguments)\n",
    "\n",
    "    if iter % train_log_iter == 0:\n",
    "\n",
    "        training_loss = False\n",
    "        evalaluation_error = False\n",
    "        training_loss = train_op.previous_minibatch_loss_average\n",
    "        evalaluation_error = train_op.previous_minibatch_evaluation_average\n",
    "        print(\"Minibatch: {0}, Loss: {1:.3f}, Error: {2:.2f}%\".format(iter, training_loss, evalaluation_error * 100))\n",
    "\n",
    "        if training_loss or evalaluation_error:\n",
    "            plotdata[\"loss\"].append(training_loss)\n",
    "            plotdata[\"error\"].append(evalaluation_error)\n",
    "            plotdata[\"iteration\"].append(iter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BkyNifqGFmZnZBvoWXUBrEbFW0j8CM0hDbD9xeJiZ1Z6a64GYmVl9qLuD6D15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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10c78763c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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u6SzgNlJyujwipkgamzbHpcCXSRdk+rEkAcsiYnjFg/n5z2GXXSperJlZX1HV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\ns0L+H1pKRQiltL12AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10b8cbe6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(plotdata[\"iteration\"], plotdata[\"loss\"], 'b--')\n",
    "plt.xlabel('Minibatch number')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('iteration run vs. Training loss')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(plotdata[\"iteration\"], plotdata[\"error\"], 'r--')\n",
    "plt.xlabel('Minibatch number')\n",
    "plt.ylabel('Label Prediction Error')\n",
    "plt.title('iteration run vs. Label Prediction Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average test error: 2.22%\n"
     ]
    }
   ],
   "source": [
    "# Test data.\n",
    "test_minibatch_size = 256\n",
    "num_samples = 10000\n",
    "num_batches_to_test = num_samples // test_minibatch_size\n",
    "test_error = 0.0\n",
    "\n",
    "for i in range(num_batches_to_test):\n",
    "\n",
    "    # Read a mini batch from the test data file\n",
    "    batch_data, batch_label = test_reader.next_batch(batch_size=test_minibatch_size)\n",
    "\n",
    "    # Evaluate\n",
    "    arguments = {input: batch_data, target: batch_data}\n",
    "    eval_error = train_op.test_minibatch(arguments=arguments)\n",
    "\n",
    "    # accumulate test error\n",
    "    test_error = test_error + eval_error\n",
    "\n",
    "# Calculation of average test error.\n",
    "average_test_error = test_error*100 / num_batches_to_test\n",
    "\n",
    "# Average of evaluation errors of all test minibatches\n",
    "print(\"Average test error: {0:.2f}%\".format(average_test_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_visualization = 12\n",
    "batch_data, batch_label = test_reader.next_batch(batch_size=test_minibatch_size)\n",
    "orig_image = batch_data[:num_visualization]\n",
    "reconstructed_image = net.eval(orig_image)*255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Original Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HWwB+RRCEXFeu8OT1cM/ZZDLxms2zyobOOgfF+uRyOb+jtlZ7XBaCIHDPuV6vv3PMdZD4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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10b8cf7828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "for i in range(12):\n",
    "    plt.subplot(3, 4, i+1)\n",
    "    plt.imshow(orig_image[i,:].reshape(28,28), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reconstructed Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VvCkdku4LaYE0NJNS6mhyyAT8qhNfu2Sh6PV6RKNRPHjwAA8ePEA0GuUG+9ed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/hS5/crQoj/KYR4JIT4TAjxry46f4/j/+VF5x8HBuG2c/zI+B2E287f+ub3pnN7wfE3cu3eWG77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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10b8d3e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(2)\n",
    "for i in range(12):\n",
    "    plt.subplot(3, 4, i+1)\n",
    "    plt.imshow(reconstructed_image[i,:].reshape(28,28), cmap='gray')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
